Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Raman Spectroscopy Instrumentation: Overview

Raman Spectroscopy Instrumentation: Overview

A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...

Raman Spectroscopy: Overview

Raman Spectroscopy: Overview

The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Vibration control of a variable-size beam supported manipulator system based on nonlinear energy sink.

Scientific reports·2026

Same author

A pilot study investigating the bacterial and fungal community shifts in facial skin in rosacea.

The Journal of international medical research·2026

Same author

Mg<sup>2+</sup>-Dependent Remodeling of Biomolecular Condensates' Microenvironments for Tunable Molecular Uptake and Altered Biochemical Dynamics.

Chem & bio engineering·2026

Same author

A novel peak-searching method for multiple radioisotopes based on deep learning.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2026

Same author

Adsorption Behavior and Mechanism of Rhodamine B on a Polyvinyl Alcohol/Carboxymethyl Chitosan Hydrogel: Integrated Experimental and Computational Study.

Molecules (Basel, Switzerland)·2026

Same author

Flower-like CoFe-LDH Activated Peroxymonosulfate for Tetracycline Degradation: Efficiency and Mechanism.

Toxics·2026

Search research articles

Related Experiment Video

Updated: May 31, 2025

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Published on: October 17, 2010

Machine learning empowered coherent Raman imaging and analysis for biomedical applications.

Yihui Zhou¹, Xiaobin Tang², Delong Zhang^2,3

¹College of Biomedical Engineering & Instrument Science, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China.

Communications Engineering

|January 24, 2025

Summary

Machine learning enhances molecular spectroscopic imaging by analyzing complex data for biological and medical insights. This review covers advancements in label-free imaging and its impact on understanding living systems.

More Related Videos

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

Implementation of a Coherent Anti-Stokes Raman Scattering CARS System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Implementation of a Coherent Anti-Stokes Raman Scattering CARS System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Published on: July 17, 2016

Related Experiment Videos

Last Updated: May 31, 2025

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Differential Imaging of Biological Structures with Doubly-resonant Coherent Anti-stokes Raman Scattering CARS

Published on: October 17, 2010

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Non-contact, Label-free Monitoring of Cells and Extracellular Matrix using Raman Spectroscopy

Published on: May 29, 2012

Implementation of a Coherent Anti-Stokes Raman Scattering CARS System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Implementation of a Coherent Anti-Stokes Raman Scattering CARS System on a Ti:Sapphire and OPO Laser Based Standard Laser Scanning Microscope

Published on: July 17, 2016

Area of Science:

Biomedical Imaging
Spectroscopy
Data Science

Background:

In situ and in vivo visualization of biomolecules is crucial for biology and medicine.
Molecular vibrational spectroscopy offers label-free, sensitive, and specific imaging.
Analyzing complex, multi-dimensional spectroscopic data remains a challenge.

Purpose of the Study:

To review recent advancements in machine learning for molecular spectroscopic imaging.
To discuss the capabilities of spectroscopic imaging modalities.
To explore the impact of machine learning on spectroscopic and other imaging techniques.

Main Methods:

Comprehensive literature review of machine learning applications in molecular spectroscopic imaging.
Analysis of spectroscopic imaging modalities and their attributes.
Discussion of data processing challenges and machine learning solutions.

Main Results:

Machine learning effectively extracts features from large spectroscopic imaging datasets.
Machine learning surpasses conventional methods in analyzing complex imaging data.
Review highlights the growing role of AI in interpreting spectroscopic data.

Conclusions:

Machine learning is a powerful tool for advancing molecular spectroscopic imaging.
Further integration of machine learning will enhance biological and medical discoveries.
This approach holds promise for revolutionizing in vivo and in situ molecular analysis.